{"uid":"cap_NXHkZKhpUEY-eQu0RBFZ4","slug":"transformation-throughput-analytics-exact-row-deduplication-explicit-9913b035","name":"Transformation Throughput Analytics — Exact Row Deduplication + Explicit Missing-Value Fill","description":"Transformation throughput — Exact row deduplication + Explicit missing-value fill: Exact row deduplication + Explicit missing-value fill for supplied transformation throughput records (processed_rows, duration_seconds). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-13-054?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","processed_rows","duration_seconds"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"dataset_group":{"type":"string","default":"","maxLength":80},"processed_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"duration_seconds":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","dataset_group":"group-a","processed_rows":10,"duration_seconds":1},{"date":"2026-10-02","row_id":"row-2","dataset_group":"group-b","processed_rows":20,"duration_seconds":2},{"date":"2026-10-03","row_id":"row-3","dataset_group":"group-a","processed_rows":15,"duration_seconds":3},{"date":"2026-10-04","row_id":"row-4","dataset_group":"group-b","processed_rows":30,"duration_seconds":4}],"maxItems":200,"minItems":1},"field":{"enum":["processed_rows","duration_seconds"],"type":"string","default":"processed_rows"},"method":{"enum":["zero","mean","median"],"type":"string","default":"median"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_loQa22n3KlG0rH0RpUx2_","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Deduplicates exact-duplicate rows and fills missing values (processed_rows or duration_seconds) in transformation throughput records using zero, mean, or median imputation, returning 2 analytical reports.","exampleAgentPrompt":"Can you clean up my transformation throughput dataset — deduplicate exact duplicate rows and fill any missing processed_rows values using median imputation? I have about 50 rows across two dataset groups.","exampleUseCases":[{"title":"ETL pipeline data quality cleanup","prompt":"I have 120 rows of ETL transformation logs with some duplicate entries and a bunch of null processed_rows values — can you deduplicate the exact duplicates and fill those nulls using the mean?"},{"title":"Benchmark duration imputation before reporting","prompt":"Before I run my throughput report, I need to clean my data: remove any fully duplicate rows and fill in missing duration_seconds with zero for my three dataset groups spanning October 2026."},{"title":"Pipeline throughput anomaly prep","prompt":"I'm preparing transformation throughput records for analysis — there are 80 rows and I suspect some duplicates and missing processed_rows. Can you deduplicate and fill the missing values using median so I get two clean reports?"}],"resultDescription":"Returns 2 analytical reports covering the deduplicated row set (exact duplicates removed) and the missing-value-filled dataset (nulls in the selected field replaced via chosen method: zero, mean, or median). Reports are computed locally with no external lookups or predictions, covering up to 200 input rows.","failureModes":["More than 200 rows submitted — request rejected or truncated","Invalid date format (not YYYY-MM-DD) — schema validation error","Missing required fields (row_id, dataset_group, date) — validation failure","Invalid field enum value (not processed_rows or duration_seconds) — schema error","Invalid method enum (not zero, mean, or median) — schema error","Empty rows array — schema validation error"],"whenToPreferThis":"Choose this endpoint when you need to simultaneously deduplicate exact-duplicate rows AND impute missing numeric values (processed_rows or duration_seconds) in transformation throughput data, and want two combined analytical reports in a single low-cost API call. Ideal for lightweight ETL data quality preprocessing pipelines handling up to 200 rows without needing external data enrichment or predictive modeling.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-28T19:42:18.914Z","isFirstParty":false,"canonicalSlug":"transformation-throughput-analytics-exact-row-deduplication-explicit-9913b035"}